Network Security Situation Assessment Research Articles

A network security situation assessment method based on fusion model

This research introduces an innovative network security situation assessment (NSSA) model, designed to overcome the shortcomings in feature extraction quality and efficiency observed in existing methods. At the core of this model is a fusion model (FM), which uniquely combines an attention mechanism with a bi-directional gated recurrent unit (BiGRU). This FM framework is adept at extracting pivotal information pertinent to various cyber threats. It employs the attention mechanism to assign appropriate weights to these crucial features, thereby elevating the model’s precision. The BiGRU, in synergy with newly proposed quantitative indicators, is responsible for generating the final evaluation results, offering a more refined measure of the cybersecurity stance. Comparative threat detection experiments reveal that the FM model exhibits superior performance across multiple evaluation metrics, marking a significant advancement in the field of network security assessment.

Research on Network Security Situation Awareness Technology Based on Security Intelligent Monitoring Technology

This paper uses data mining technology to dynamically monitor tobacco Industrial Enterprise' information systems. This paper builds an Internet security situation awareness system under a big data environment. The weight clustering method is used to classify users' network behavior. The spacing of weights is optimized to ensure the maximum difference in classification. Then, NAWL-ILSTM technology establishes a security situational awareness model for the Internet environment. In this project, the extended and short-memory Nadam optimal algorithm (NAWL) is used to realize data deep learning. Finally, the tobacco industry network security situation assessment method is designed to complete the dynamic monitoring of tobacco industry network security based on data mining. Simulation results show that the proposed method can effectively improve the safety evaluation performance of the system and reduce evaluation errors.

Deep learning with blockchain based cyber security threat intelligence and situational awareness system for intrusion alert prediction

Network security situation assessment (NSSA) is imperative and active defense technology in the network security situation. By examining NSSA data, one can examine the threat of network security and examine the network attack phase and hence fully grasp the complete network security situation. With the quick design of 5 G, the cloud model and Internet of things (IoT), the network platform is increasingly complicated and resulting in diversity of network threats which discover the accuracy. Thus, a new blockchain based cyber-security threat intelligence (CTI) and situational awareness system is devised for intrusion alert prediction. A blockchain-based CTI model is considered where data acquired are allowed to linear normalization. Here, the cyber situational awareness engine is used for alert segregation, which is implemented with entropy weighting power k means algorithm wherein weights generated during alert segregation are updated using Adaptive Transit Search (ATS). Then, the feature selection is implemented using hybrid Soergel and Lorentzian. The selected features are fed to Deep Maxout Network (DMN) for performing intrusion alert prediction. Finally, the cyber attack mitigation is carried out by blacklisting based on predicted result. The modified DMN outperformed with highest F-measure of 95.2%, precision of 96.9% and recall of 94.7%.

Research on Hierarchical Network Security Situational Awareness Data Fusion Method in Big Data Environment

Abstract With the globalization of information and the development of big data technology, people rely more and more on the network. The network brings convenience to everyone at the same time, and many security problems also come. This paper first introduces the NSSA model from different perspectives and analyzes the characteristics of its application, combined with the information fusion to put forward the H-NSSA system model, and constructs the framework of the network security posture assessment. Then, we assess the credibility of the intrusion behaviors in the network, classify and fuse the relevant evidence, and optimize the evidence conflict synthesis rules on this basis to improve the confidence of the event. The Bayesian network model is constructed by describing the causal relationship between elements using the assignment relationship model. Finally, experimental simulation and practical application analysis methods are used to study the network security situation and situation assessment, as well as the practical application of the actual situation in enterprises. The results show that the network suffered a large degree of attack in the 9th-12th time window and a medium degree of attack in the 42nd-43rd time window, and the H-NSSA algorithm model can reach asymptotic convergence in only 68 iterations, and the performance of the algorithm is high.

Network security situation assessment and prediction method based on multimodal transformation in edge computing

Network security play a critical role in ensuring the robustness and integrity of network systems. We propose a novel method based on multimodal transformation in edge computing for accurate and reliable data communication. The method leverages Graph Convolutional Networks (GCNs) to capture and analyze the complex relationships and dependencies among network entities, enabling enhanced prediction capabilities. By integrating multimodal transformation, diverse data sources are fused in the model. The assessments and comprehensive comparisons with established algorithms unequivocally establish the supremacy of our proposed approach, particularly with respect to metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Our method exhibits promising performance, enabling proactive threat detection and mitigation in network security by more then 90% accuracy. In the future work, we have included the different multimodal transformation techniques, addressing interoperability challenges, scaling the method for large-scale networks, and adapting the model to dynamic network environments.

Research on Network Security Situation Assessment Model in Decentralized Computing Environment

Abstract In this paper, decentralized computing is used as an entry point to review the latest research results on network security issues through two methods, namely task assignment and hierarchical analysis, and construct a network security posture assessment model based on decentralized computing. A control framework is constructed by utilizing the functional complementarity of continuous authentication and security threat assessment in order to facilitate real-time observation of the security situation of the network and timely elimination of malicious nodes. A quantitative network security threat posture assessment model is constructed through hierarchical analysis to observe the extent of the breach of confidentiality and integrity of network information based on the security threat posture index. The effectiveness of the network security posture assessment model and method proposed in this paper was verified by empirical analysis in a simulated environment. The results show that after the simulated attack lasts for 12 minutes, the network security risk index measured by the assessment model in the test cascade case becomes larger with the intensification of the network attack, and the risk index value is up to 5.5. In summary, the network security posture assessment model based on decentralized computing designed in this paper can quickly reflect the changes in the security status of the underlying network and provide administrators with the current security status of the network in a macroscopic way.

A novel network security situation assessment model based on multiple strategies whale optimization algorithm and bidirectional GRU.

The rapid development of the internet has brought about a comprehensive transformation in human life. However, the challenges of cybersecurity are becoming increasingly severe, necessitating the implementation of effective security mechanisms. Cybersecurity situational awareness can effectively assess the network status, facilitating the formulation of better cybersecurity defense strategies. However, due to the low accuracy of existing situational assessment methods, situational assessment remains a challenge. In this study, a new situational assessment method, MSWOA-BiGRU, combining optimization algorithms and temporal neural networks, was proposed. Firstly, a scientific indicator system proposed in this research is used to calculate the values of each indicator. Then, the Analytic Hierarchy Process is used to derive the actual situation values, which serve as labels. Taking into account the temporal nature of network traffic, the BiGRU model is utilized for cybersecurity situational assessment. After integrating time-related features and network traffic characteristics, the situational assessment value is obtained. During the evaluation process, a whale optimization algorithm (MSWOA) improved with a mix of strategies proposed in this study was employed to optimize the model. The performance of the proposed MSWOA-BiGRU model was evaluated on publicly available real network security datasets. Experimental results indicate that compared to traditional optimization algorithms, the optimization performance of MSWOA has seen significant enhancement. Furthermore, MSWOA-BiGRU demonstrates superior performance in cybersecurity situational assessment compared to existing evaluation methods.

Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM

In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman’s rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status.

AHP-Based Network Security Situation Assessment for Industrial Internet of Things

The Industrial Internet of Things (IIoT) is used in various industries to achieve industrial automation and intelligence. Therefore, it is important to assess the network security situation of the IIoT. The existing network situation assessment methods do not take into account the particularity of the IIoT’s network security requirements and cannot achieve accurate assessment. In addition, IIoT transmits a lot of heterogeneous data, which is subject to cyber attacks, and existing classification methods cannot effectively deal with unbalanced data. To solve the above problems, this paper first considers the special network security requirements of the IIoT, and proposes a quantitative evaluation method of network security based on the Analytic Hierarchy Process (AHP). Then, the average under-/oversampling (AUOS) method is proposed to solve the problem of unbalance of network attack data. Finally, an IIoT network security situation assessment classifier based on the eXtreme Gradient Boosting (XGBoost) is constructed. Experiments show that the situation assessment method proposed in this paper can more accurately characterize the network security state of the IIoT. The AUOS method can achieve data balance without generating too much data, and does not burden the training of the model. The classifier constructed in this paper is superior to the traditional classification algorithm.

Network Security Prediction and Situational Assessment Using Neural Network-based Method

Technology development has promoted network construction, but malicious network attacks are still inevitable. To solve the problem that the current network security assessment is not practical and the assessment effect is poor, this study proposes a network security monitoring tool based on situation assessment and prediction to assist network security construction. The framework of the evaluation module is based on convolution neural network. The initial module is introduced to convert some large convolution cores into small convolution cores in series. This is to reduce the operating cost, because building multiple evaluators in series can maximize the retention of characteristic values. This module is the optimized form of Elman neural network. The delay operator is added to the model to respond to the time property of network attack. At the same time, particle swarm optimization algorithm is used to solve the initial weight dependence problem. The research adopts two methods of security situation assessment and situation prediction to carry out model application test. During the test, the commonly used KDD Cup99 is used as intrusion detection data. The experimental results of the network security situation evaluation module show that the optimization reduces the evaluation error by 3.34%, and the accuracy meets the evaluation requirements. The model is superior to the back propagation neural network and the standard Elman model. The model proposed in this study achieves better prediction of posture scores from 0.3 to 0.9, which is more stable than BP neural network. It proves that the model designed by the research can achieve more stable and higher prediction than similar models. It is more practical to obtain better results on the basis of a more stable model architecture and lower implementation costs, which is a meaningful attempt in the wide application of network security.

Network security situation assessment based on dual attention mechanism and HHO-ResNeXt

The traditional convolutional neural network (CNN) has a limited receptive field and cannot accurately identify the importance of each channel, making it difficult to solve increasingly complex network security problems. To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions. The optimal hyperparameters of the model are selected by the Harris Hawks Optimization (HHO) algorithm. The model can accurately obtain the importance of each channel to assign weights to each channel while making full use of the rich contexts among neighbour keys, effectively enhancing the convolutional neural network. Furthermore, this paper calculates the network security situation value (NSSV) of the adopted datasets based on attack impact. Lastly, experiments on two cybersecurity datasets show that the comprehensive performance of the model on the three indicators of accuracy, precision and F-scores, as well as network security situation assessment, are superior to other models.

An Algorithm for Network Security Situation Assessment Based on Deep Learning

Aiming at the problems that the existing assessment methods are difficult to solve, such as the low efficiency and uncertainty of network security situation assessment in complex network environment, by constructing the characteristic elements of network security big data, a typical model based on deep learning, long short-term memory (LSTM), is established to assess the network security situation in time series. The hidden relationship and change trend of network security situation are automatically mined and analyzed through the deep learning algorithm of big data, which greatly improves the prediction accuracy of security situation. Experimental analysis shows that this method has a better assessment effect on network threats, has higher learning efficiency than the traditional network situation assessment methods, and has strong representation ability in the face of network threats. It can more accurately and effectively assess the changing trend of big data security situation in the future.

Network Situation Assessment Method Based on Improved BP Neural Network

Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.

A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet, this paper adopts the evidential reasoning (ER)algorithm and belief rule base (BRB) method to establish an assessment model. First, this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge. Second, the evaluation indicators are fused with expert knowledge and the ER algorithm. According to the fusion results, a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used to optimize the model parameters. This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion. Moreover, it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data. Finally, a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method. The research results show that this method has strong applicability to the network security situation assessment of complex Industrial Internet systems. It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures, thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

Construction of Network Culture Security Indicator System Based on Deep Learning Algorithm

The Internet has become a key information infrastructure in China. However, in recent years, China's network security incidents have been frequent, thus posing a great threat to relevant software, hardware and other information infrastructure. It is very important to establish a large-scale network security situation analysis system based on existing network security infrastructure and technology to improve the processing capabilities of controlling, analyzing, and predicting network security situations. Network security situation assessment is an emerging research hotspot that has emerged in recent years. This article first defined a series of concepts such as the security indicator ring system, network culture, and deep learning algorithms, and then conducted comparative analysis through experiments. Compared with the original dataset, the classification accuracy after feature selection increased by 33.6%.

Speech denoising algorithm and artificial intelligence application in network security

Speech denoising refers to people finding useful information in noise-affected speech data and processing it. This technology is an important part of speech signal processing technology. With the continuous progress of science and technology, its application in various fields is gradually improving, among which voice noise reduction technology has also been well developed. Especially in recent years, with the rapid development of artificial intelligence science and technology, anti-network speech denoising technology based on deep learning has been derived. Compared with traditional speech denoising techniques, it has better performance in resolving speech data distortion and denoising results. However, in practical applications, this method still has some shortcomings, such as: some unique noises are not removed, and the effective information extraction is distorted under the background of high-intensity noise. In response to these outstanding problems, this research is based on the anti-network speech denoising technology, combined with the Wiener filter speech denoising algorithm, and applies artificial intelligence technology to network security technology management. In this paper, two algorithms, COVL and SSNR, are used to compare the original input speech data signal with noise and the speech data signal preprocessed by Wiener filtering. The additional information is input into the generation network and the discriminant network, and the additional information is used to guide the data generation. The least squares loss function of the binary coding algorithm is used to replace the cross entropy loss function, so that the original discriminant network loss function is not changed, which improves the accuracy of discrimination. This improved technology is superior in terms of speed, productivity and improved accuracy. While building the system, mature network technologies and equipment applications are fully applied, and speech de-noising technology is used for network security situation assessment and prediction simulation tests. The simulation test results show that the network security situation assessment is reasonable and effective, and the prediction is accurate in real time.

Information security of flowmeter communication network based on multi-sensor data fusion

With the progress of society, the use of intelligent flowmeters has become more and more widespread. The powerful functions of intelligent flowmeters have made people’s production and living more convenient. Still, at the same time, public data, including industry and agriculture privacy and information security, have exposed huge security risks. This paper aims to study the information security of flowmeter communication networks based on multi-sensor data fusion and artificial intelligence-driven. The present study introduces network information security and its development direction, proposes a cloud computing-based network security situation prediction algorithm, uses the cloud model to predict the security situation of the cloud computing network, and gives a cloud computing network security situation assessment system model. At the same time, it analyzes and explores network information security, and explores network security based on multi-sensor data fusion and artificial intelligence driving. The experimental research results show that due to the unique advantages of the universal flowmeter intelligent processing system, such as high resource utilization, uniform standards, and wide application range, it has a lot of room for development. With the development of science and technology and application fields, and the continuous progress and expansion of general flowmeters, the future space of general category flowmeters is huge. The popularity of the Internet is getting faster and faster, and the requirements for information security are getting higher and higher, and the information security issues faced in the future will become more and more complicated. Therefore, it is necessary to speed up the construction of an information security system, and take measures to strictly manage from the aspects of law, management, technology and talents.

Network Security Situation Assessment Based on Attack Graph Techniques

Network security situation awareness (NSSA) can analyze current network status and predict trends. Intrusion detection systems are used as sources of security factor in situational awareness, and their accuracy affects the assessment of network security. The attack graph can filter out key nodes and enumerate possible attack paths, which has become the main method of risk assessment. Therefore, a network security situation assessment technology based on intrusion detection was proposed. The detection rate of the intrusion detection system was improved firstly, and then the attack graph was combined with the hidden Markov model (HMM) for network security assessment. The experimental results show that this method can effectively speculate the attack intention and reflect the results intuitively and roundly.

Development of Network Security Based on the Neural Network PSD Algorithm.

The more frequent occurrence of network security incidents has an impact on network security. Through the research on network security situational awareness, this paper constructs a multilevel network security situation evaluation index system from various aspects and uses the Elman neural network optimized by the genetic algorithm to evaluate network security situation. Aiming at the disadvantage of subjective dependence in the traditional assignment method of basic probability assignment function, Elman neural network is used to obtain the basic probability assignment function to increase its objectivity, and it is optimized with the PSD algorithm. In addition, the neural network is further improved by the genetic algorithm. In the traditional D-S evidence theory, an evidence correction step is added to optimize the situation that the final judgment result is incorrect due to evidence conflict. Finally, the fusion rules of the D-S evidence theory are used to fuse the support degrees of the four first-level situations to different security levels to obtain the final network security situation assessment result. The results show that the prediction accuracy of the GA-Elman neural network model is as high as 80%, which is significantly higher than that of the traditional D-S model, indicating that the model proposed in this paper has improved the accuracy of the assessment and prediction results. In conclusion, this study provides feasible theoretical prediction guidance for the accurate assessment of network security posture, reveals the improvement ideas for network security development, and is of great significance for the maintenance of network environment security.

The Technical Research on the Assessment of Network Security Situation Based on D-S Evidence Theory

Abstract With the increasing importance of network security, more attention is paid to the research on network security situation awareness, while network security situation assessment is the core of situation awareness. The existing situation assessment technology has many problems, such as unitary methodology, large-scale subjective factors and so on, affecting the accuracy of situation assessment. Combined with the traditional network security technology, this paper sets up a three-layer evaluation index system, and puts forward an overall network security situation evaluation model based on hidden Markov model, PageRank algorithm and D-S evidence theory, through which, the overall situation is evaluated after using different fusion algorithms in different fusion stages. The simulation result shows that the above assessment method is more accurate and efficient than the method using single model.